{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd97b4cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单号</th>\n",
       "      <th>产品码</th>\n",
       "      <th>消费日期</th>\n",
       "      <th>产品说明</th>\n",
       "      <th>数量</th>\n",
       "      <th>单价</th>\n",
       "      <th>用户码</th>\n",
       "      <th>城市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536374</td>\n",
       "      <td>21258</td>\n",
       "      <td>6/1/2022 9:09</td>\n",
       "      <td>绿联usb分线器 一拖四</td>\n",
       "      <td>32</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15100</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536376</td>\n",
       "      <td>22114</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>加大男装T恤男大码胖子宽松卡</td>\n",
       "      <td>48</td>\n",
       "      <td>50.45</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536376</td>\n",
       "      <td>21733</td>\n",
       "      <td>6/1/2022 9:32</td>\n",
       "      <td>唐装男夏季青年棉麻中国风加肥</td>\n",
       "      <td>64</td>\n",
       "      <td>86.55</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536378</td>\n",
       "      <td>22386</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>越南进口白心火龙果4个装</td>\n",
       "      <td>10</td>\n",
       "      <td>108.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536378</td>\n",
       "      <td>85099C</td>\n",
       "      <td>6/1/2022 9:37</td>\n",
       "      <td>大连美早樱桃400g 果径约26mm</td>\n",
       "      <td>10</td>\n",
       "      <td>166.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      订单号     产品码           消费日期                产品说明  数量      单价    用户码  城市\n",
       "0  536374   21258  6/1/2022 9:09        绿联usb分线器 一拖四  32   10.95  15100  北京\n",
       "1  536376   22114  6/1/2022 9:32      加大男装T恤男大码胖子宽松卡  48   50.45  15291  上海\n",
       "2  536376   21733  6/1/2022 9:32      唐装男夏季青年棉麻中国风加肥  64   86.55  15291  上海\n",
       "3  536378   22386  6/1/2022 9:37        越南进口白心火龙果4个装  10  108.95  14688  北京\n",
       "4  536378  85099C  6/1/2022 9:37  大连美早樱桃400g 果径约26mm  10  166.95  14688  北京"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd #导入Pandas\n",
    "df_sales = pd.read_csv('电商历史订单.csv') #载入数据\n",
    "df_sales.head() #显示头几行数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae998f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #导入Matplotlib的pyplot模块\n",
    "# 设置字体为SimHei，以正常显示中文标签\n",
    "plt.rcParams[\"font.family\"]=['SimHei'] \n",
    "plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "# 用来正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "#构建月度的订单数的DataFrame\n",
    "df_sales['消费日期'] = pd.to_datetime(df_sales['消费日期']) #转化日期格式\n",
    "df_orders_monthly = df_sales.set_index('消费日期')['订单号'].resample('M').nunique()\n",
    "#设定绘图的画布\n",
    "ax = pd.DataFrame(df_orders_monthly.values).plot(grid=True,figsize=(12,6),legend=False)\n",
    "ax.set_xlabel('月份') # X轴label\n",
    "ax.set_ylabel('订单数') # Y轴Label\n",
    "ax.set_title('月度订单数') # 图题\n",
    "#设定X轴月份显示格式\n",
    "plt.xticks(\n",
    "    range(len(df_orders_monthly.index)), \n",
    "    [x.strftime('%m.%Y') for x in df_orders_monthly.index], \n",
    "    rotation=45)\n",
    "plt.show() # 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "afc44bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sales = df_sales.drop_duplicates() #删除重复的数据行\n",
    "df_sales = df_sales.loc[df_sales['数量'] > 0] #清洗掉数量小于等于0的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384cb4a8",
   "metadata": {},
   "source": [
    "## 计算并添加总价信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73d39a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>订单号</th>\n",
       "      <th>产品码</th>\n",
       "      <th>消费日期</th>\n",
       "      <th>产品说明</th>\n",
       "      <th>数量</th>\n",
       "      <th>单价</th>\n",
       "      <th>用户码</th>\n",
       "      <th>城市</th>\n",
       "      <th>总价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536374</td>\n",
       "      <td>21258</td>\n",
       "      <td>2022-06-01 09:09:00</td>\n",
       "      <td>绿联usb分线器 一拖四</td>\n",
       "      <td>32</td>\n",
       "      <td>10.95</td>\n",
       "      <td>15100</td>\n",
       "      <td>北京</td>\n",
       "      <td>350.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536376</td>\n",
       "      <td>22114</td>\n",
       "      <td>2022-06-01 09:32:00</td>\n",
       "      <td>加大男装T恤男大码胖子宽松卡</td>\n",
       "      <td>48</td>\n",
       "      <td>50.45</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "      <td>2421.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536376</td>\n",
       "      <td>21733</td>\n",
       "      <td>2022-06-01 09:32:00</td>\n",
       "      <td>唐装男夏季青年棉麻中国风加肥</td>\n",
       "      <td>64</td>\n",
       "      <td>86.55</td>\n",
       "      <td>15291</td>\n",
       "      <td>上海</td>\n",
       "      <td>5539.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536378</td>\n",
       "      <td>22386</td>\n",
       "      <td>2022-06-01 09:37:00</td>\n",
       "      <td>越南进口白心火龙果4个装</td>\n",
       "      <td>10</td>\n",
       "      <td>108.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "      <td>1089.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536378</td>\n",
       "      <td>85099C</td>\n",
       "      <td>2022-06-01 09:37:00</td>\n",
       "      <td>大连美早樱桃400g 果径约26mm</td>\n",
       "      <td>10</td>\n",
       "      <td>166.95</td>\n",
       "      <td>14688</td>\n",
       "      <td>北京</td>\n",
       "      <td>1669.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      订单号     产品码                消费日期                产品说明  数量      单价    用户码  \\\n",
       "0  536374   21258 2022-06-01 09:09:00        绿联usb分线器 一拖四  32   10.95  15100   \n",
       "1  536376   22114 2022-06-01 09:32:00      加大男装T恤男大码胖子宽松卡  48   50.45  15291   \n",
       "2  536376   21733 2022-06-01 09:32:00      唐装男夏季青年棉麻中国风加肥  64   86.55  15291   \n",
       "3  536378   22386 2022-06-01 09:37:00        越南进口白心火龙果4个装  10  108.95  14688   \n",
       "4  536378  85099C 2022-06-01 09:37:00  大连美早樱桃400g 果径约26mm  10  166.95  14688   \n",
       "\n",
       "   城市      总价  \n",
       "0  北京   350.4  \n",
       "1  上海  2421.6  \n",
       "2  上海  5539.2  \n",
       "3  北京  1089.5  \n",
       "4  北京  1669.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales['总价'] = df_sales['数量'] * df_sales['单价'] #计算每单的总价\n",
    "df_sales.head() #显示头几行数据 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de66050d",
   "metadata": {},
   "source": [
    "## 构建用户表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d419777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>16015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>16016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>16017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>16018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>16019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码\n",
       "0    14681\n",
       "1    14682\n",
       "2    14684\n",
       "3    14687\n",
       "4    14688\n",
       "..     ...\n",
       "975  16015\n",
       "976  16016\n",
       "977  16017\n",
       "978  16018\n",
       "979  16019\n",
       "\n",
       "[980 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = pd.DataFrame(df_sales['用户码'].unique()) #生成以用户码为主键的结构df_user\n",
    "df_user.columns = ['用户码'] #设定字段名\n",
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) #按用户码排序\n",
    "df_user #显示df_user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36406205",
   "metadata": {},
   "source": [
    "## 为每个用户求出RMF值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1cd99016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值\n",
       "0  14681   70\n",
       "1  14682  187\n",
       "2  14684   25\n",
       "3  14687  106\n",
       "4  14688    7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales['消费日期'] = pd.to_datetime(df_sales['消费日期']) #转化日期格式\n",
    "df_recent_buy = df_sales.groupby('用户码').消费日期.max().reset_index() #构建消费日期信息\n",
    "df_recent_buy.columns = ['用户码','最近日期'] #设定字段名\n",
    "df_recent_buy['R值'] = (df_recent_buy['最近日期'].max() - df_recent_buy['最近日期']).dt.days #计算最新日期与上次消费日期的天数\n",
    "df_user = pd.merge(df_user, df_recent_buy[['用户码','R值']], on='用户码') #把上次消费距最新日期的天数（R值）合并至df_user结构\n",
    "df_user.head() #显示df_user头几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbd346c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>F值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
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       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值   F值\n",
       "0  14681   70    7\n",
       "1  14682  187    2\n",
       "2  14684   25  390\n",
       "3  14687  106   15\n",
       "4  14688    7  327"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequency = df_sales.groupby('用户码').消费日期.count().reset_index() #计算每个用户消费次数，构建df_frequency对象\n",
    "df_frequency.columns = ['用户码','F值'] #设定字段名称\n",
    "df_user = pd.merge(df_user, df_frequency, on='用户码') #把消费频率整合至df_user结构\n",
    "df_user.head() #显示头几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d4682cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>F值</th>\n",
       "      <th>M值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>498.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>52.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>390</td>\n",
       "      <td>1201.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>15</td>\n",
       "      <td>628.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>327</td>\n",
       "      <td>18807.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值   F值        M值\n",
       "0  14681   70    7    498.95\n",
       "1  14682  187    2     52.00\n",
       "2  14684   25  390   1201.51\n",
       "3  14687  106   15    628.38\n",
       "4  14688    7  327  18807.60"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_revenue = df_sales.groupby('用户码').总价.sum().reset_index() #根据消费总额，构建df_revenue对象\n",
    "df_revenue.columns = ['用户码','M值'] #设定字段名称\n",
    "df_user = pd.merge(df_user, df_revenue, on='用户码') #把消费金额整合至df_user结构\n",
    "df_user.head() #显示头几行数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f7c4e8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': '消费金额分布直方图'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_user['R值'].plot(kind='hist', bins=20, title = '新进度分布直方图') #R值直方图\n",
    "df_user.query('F值 < 800')['F值'].plot(kind='hist', bins=50, title = '消费频率分布直方图') #F值直方图\n",
    "df_user.query('M值 < 20000')['M值'].plot(kind='hist', bins=50, title = '消费金额分布直方图') #M值直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec3b5fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans #导入KMeans模块\n",
    "def show_elbow(df, ax, title): \n",
    "    distance_list = [] \n",
    "    K = range(1,9)\n",
    "    for k in K:\n",
    "        kmeans = KMeans(n_clusters=k, max_iter=100)\n",
    "        kmeans = kmeans.fit(df)\n",
    "        distance_list.append(kmeans.inertia_)\n",
    "    ax.plot(K, distance_list, 'bx-')\n",
    "    ax.set_xlabel('k')\n",
    "    ax.set_ylabel('距离均方误差')\n",
    "    ax.set_title(title)\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "show_elbow(df_user[['R值']], axes[0], 'R值聚类K值手肘图')\n",
    "show_elbow(df_user[['F值']], axes[1], 'F值聚类K值手肘图')\n",
    "show_elbow(df_user[['M值']], axes[2], 'M值聚类K值手肘图')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d11359d",
   "metadata": {},
   "source": [
    "## 创建和训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be6fb7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans #导入KMeans模块\n",
    "kmeans_R = KMeans(n_clusters=3) #设定K=3\n",
    "kmeans_F = KMeans(n_clusters=4) #设定K=4\n",
    "kmeans_M = KMeans(n_clusters=4) #设定K=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b95ba2f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\huangj2.ARES\\AppData\\Local\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_R.fit(df_user[['R值']]) #拟合模型\n",
    "kmeans_F.fit(df_user[['F值']]) #拟合模型\n",
    "kmeans_M.fit(df_user[['M值']]) #拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a67691c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>F值</th>\n",
       "      <th>M值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值层级</th>\n",
       "      <th>M值层级</th>\n",
       "    </tr>\n",
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       "      <td>25</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>7</td>\n",
       "      <td>327</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值   F值        M值  R值层级  F值层级  M值层级\n",
       "0  14681   70    7    498.95     0     0     0\n",
       "1  14682  187    2     52.00     2     0     0\n",
       "2  14684   25  390   1201.51     0     2     0\n",
       "3  14687  106   15    628.38     2     0     0\n",
       "4  14688    7  327  18807.60     0     2     2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user['R值层级'] = kmeans_R.predict(df_user[['R值']]) #通过聚类模型求出R值的层级\n",
    "df_user['F值层级'] = kmeans_F.predict(df_user[['F值']]) #通过聚类模型求出F值的层级\n",
    "df_user['M值层级'] = kmeans_M.predict(df_user[['M值']]) #通过聚类模型求出M值的层级\n",
    "df_user.head() #显示头几行数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c977821",
   "metadata": {},
   "source": [
    "## 给聚类后的层级排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2af07f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义一个order_cluster函数为聚类排序\n",
    "def order_cluster(cluster_name, target_name,df,ascending=False):\n",
    "    new_cluster_name = 'new_' + cluster_name #新的聚类名称\n",
    "    df_new = df.groupby(cluster_name)[target_name].mean().reset_index() #按聚类结果分组，创建df_new对象\n",
    "    df_new = df_new.sort_values(by=target_name,ascending=ascending).reset_index(drop=True) #排序\n",
    "    df_new['index'] = df_new.index #创建索引字段\n",
    "    df_new = pd.merge(df,df_new[[cluster_name,'index']], on=cluster_name) #基于聚类名称把df_new还原为df对象，并添加索引字段\n",
    "    df_new = df_new.drop([cluster_name],axis=1) #删除聚类名称\n",
    "    df_new = df_new.rename(columns={\"index\":cluster_name}) #将索引字段重命名为聚类名称字段\n",
    "    return df_new #返回排序后的df_new对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21a928fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>F值</th>\n",
       "      <th>M值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值层级</th>\n",
       "      <th>M值层级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14681</td>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "      <td>498.95</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>52.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>390</td>\n",
       "      <td>1201.51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>15</td>\n",
       "      <td>628.38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>327</td>\n",
       "      <td>18807.60</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     用户码   R值   F值        M值  R值层级  F值层级  M值层级\n",
       "0  14681   70    7    498.95     2     0     0\n",
       "1  14682  187    2     52.00     1     0     0\n",
       "2  14684   25  390   1201.51     2     2     0\n",
       "3  14687  106   15    628.38     1     0     0\n",
       "4  14688    7  327  18807.60     2     2     2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user = order_cluster('R值层级', 'R值', df_user, False) #调用簇排序函数\n",
    "df_user = order_cluster('F值层级', 'F值', df_user, True) #调用簇排序函数\n",
    "df_user = order_cluster('M值层级', 'M值', df_user, True) #调用簇排序函数\n",
    "df_user = df_user.sort_values(by='用户码',ascending=True).reset_index(drop=True) #根据用户码排序\n",
    "df_user.head() #显示头几行数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c603a5",
   "metadata": {},
   "source": [
    "## 为用户整体分组画像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b566bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>R值</th>\n",
       "      <th>F值</th>\n",
       "      <th>M值</th>\n",
       "      <th>R值层级</th>\n",
       "      <th>F值层级</th>\n",
       "      <th>M值层级</th>\n",
       "      <th>总分</th>\n",
       "      <th>总体价值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14682</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>52.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14684</td>\n",
       "      <td>25</td>\n",
       "      <td>390</td>\n",
       "      <td>1201.51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14687</td>\n",
       "      <td>106</td>\n",
       "      <td>15</td>\n",
       "      <td>628.38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14688</td>\n",
       "      <td>7</td>\n",
       "      <td>327</td>\n",
       "      <td>18807.60</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>高价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>16015</td>\n",
       "      <td>3</td>\n",
       "      <td>181</td>\n",
       "      <td>704.55</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>16016</td>\n",
       "      <td>2</td>\n",
       "      <td>224</td>\n",
       "      <td>1465.51</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>16017</td>\n",
       "      <td>46</td>\n",
       "      <td>32</td>\n",
       "      <td>211.88</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>16018</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>408.90</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>低价值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>16019</td>\n",
       "      <td>46</td>\n",
       "      <td>160</td>\n",
       "      <td>3786.24</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>中价值</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码   R值   F值        M值  R值层级  F值层级  M值层级  总分 总体价值\n",
       "0    14681   70    7    498.95     2     0     0   2  低价值\n",
       "1    14682  187    2     52.00     1     0     0   1  低价值\n",
       "2    14684   25  390   1201.51     2     2     0   4  中价值\n",
       "3    14687  106   15    628.38     1     0     0   1  低价值\n",
       "4    14688    7  327  18807.60     2     2     2   6  高价值\n",
       "..     ...  ...  ...       ...   ...   ...   ...  ..  ...\n",
       "975  16015    3  181    704.55     2     1     0   3  中价值\n",
       "976  16016    2  224   1465.51     2     1     0   3  中价值\n",
       "977  16017   46   32    211.88     2     0     0   2  低价值\n",
       "978  16018   38   28    408.90     2     0     0   2  低价值\n",
       "979  16019   46  160   3786.24     2     1     1   4  中价值\n",
       "\n",
       "[980 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user['总分'] = df_user['R值层级'] + df_user['F值层级'] + df_user['M值层级'] #求出每个用户RFM总分\n",
    "#在df_user对象中添加总体价值这个字段\n",
    "df_user.loc[(df_user['总分']<=2) & (df_user['总分']>=0), '总体价值'] = '低价值' \n",
    "df_user.loc[(df_user['总分']<=4) & (df_user['总分']>=3), '总体价值'] = '中价值' \n",
    "df_user.loc[(df_user['总分']<=8) & (df_user['总分']>=5), '总体价值'] = '高价值'\n",
    "df_user #显示df_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f98f4e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2097db17910>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示高、中、低价值组分布散点图（F值与M值）\n",
    "plt.scatter(df_user.query(\"总体价值 == '高价值'\")['F值'],\n",
    "                   df_user.query(\"总体价值 == '高价值'\")['M值'],c='g',marker='*')\n",
    "plt.scatter(df_user.query(\"总体价值 == '中价值'\")['F值'],\n",
    "                   df_user.query(\"总体价值 == '中价值'\")['M值'],marker=8)\n",
    "plt.scatter(df_user.query(\"总体价值 == '低价值'\")['F值'],\n",
    "                   df_user.query(\"总体价值 == '低价值'\")['M值'],c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9325fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6)) # 图片大小\n",
    "ax = plt.subplot(111, projection='3d') # 坐标系\n",
    "ax.scatter(df_user.query(\"总体价值 == '高价值'\")['R值'], # 散点图\n",
    "df_user.query(\"总体价值 == '高价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '高价值'\")['M值'], c='g',marker='*')\n",
    "ax.scatter(df_user.query(\"总体价值 == '中价值'\")['R值'],\n",
    "df_user.query(\"总体价值 == '中价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '中价值'\")['M值'], marker=8)\n",
    "ax.scatter(df_user.query(\"总体价值 == '低价值'\")['R值'],\n",
    "df_user.query(\"总体价值 == '低价值'\")['F值'],\n",
    "df_user.query(\"总体价值 == '低价值'\")['M值'], c='r')\n",
    "ax.set_xlabel('R值') # 坐标轴\n",
    "ax.set_ylabel('F值') # 坐标轴\n",
    "ax.set_zlabel('M值') # 坐标轴\n",
    "plt.show() # 输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca8d76a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
